Authors: Bissantz, Nicolai
Chown, Justin
Dette, Holger
Title: Regularization parameter selection in indirect regression by residual based bootstrap
Language (ISO): en
Abstract: Residual-based analysis is generally considered a cornerstone of statistical methodology. For a special case of indirect regression, we investigate the residual-based empirical distribution function and provide a uniform expansion of this estimator, which is also shown to be asymptotically most precise. This investigation naturally leads to a completely data-driven technique for selecting a regularization parameter used in our indirect regression function estimator. The resulting methodology is based on a smooth bootstrap of the model residuals. A simulation study demonstrates the effectiveness of our approach.
Subject Headings: bandwidth selection
smooth bootstrap
residual-based empirical distribution function
regularization
indirect nonparametric regression
deconvolution function estimator
Subject Headings (RSWK): Nichtparametrische Regression
Bootstrap-Statistik
URI: http://hdl.handle.net/2003/35302
http://dx.doi.org/10.17877/DE290R-17345
Issue Date: 2016
Appears in Collections:Sonderforschungsbereich (SFB) 823

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